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2020 – today
- 2026
[c13]Mingjing Sun
, Torsten Braun, Eric Samikwa:
FAST: Floating AI Service for Time-Varying Mobile Mixed Reality Networks. ICC 2026: 1-6- [c12]Sayantini Majumdar, Eric Samikwa, Konstantinos Samdanis, Emmanouil Pateromichelakis, Elham Hashemi Nezhad, Torsten Braun:
EnSplit: Dynamic DRL Energy-Aware Split Inference for AI-Based UE Apps in 6G Networks. NetSoft 2026: 82-89 - [c11]Elham Hashemi Nezhad, Eric Samikwa, Torsten Braun:
Decentralized Federated Multi-Agent Reinforcement Learning for RAN Controller Orchestration in 6G. NetSoft 2026: 180-188 - [c10]Solomon Fikadie Wassie, Eric Samikwa, Torsten Braun, Slawomir Kuklinski, David Jia, Véronique Capdevielle:
Attention-Based Actor-Critic DRL for Online Service Function Chain Composition in 6G Networks. IFIP Networking 2026: 1-9 - [c9]Sabyasachi Banik, Eric Samikwa, Torsten Braun:
Resource-Aware Federated Transfer Learning with Adaptive Model Scaling and Channel Attention. PerCom Workshops 2026: 1-6 - [c8]Bruno S. Martins, Eric Samikwa, Torsten Braun, Denis Rosário, Eduardo Cerqueira, Leandro Villas:
FedLoad: Adaptive Partial Training for Model Heterogeneous Federated Learning. WCNC 2026: 1-6 - 2025
- [j5]George Hope Chidziwisano, Eric Samikwa, Chisomo Daka:
Deep learning methods for poultry disease prediction using images. Comput. Electron. Agric. 230: 109765 (2025) - [j4]Zimu Xu, Antonio Di Maio, Eric Samikwa, Torsten Braun:
CSTAR-FL: Stochastic Client Selection for Tree All-Reduce Federated Learning. IEEE Trans. Mob. Comput. 24(4): 3110-3129 (2025) - [c7]Solomon Fikadie Wassie, Eric Samikwa, Antonio Di Maio, Torsten Braun:
MARC-6G: Multi-Agent Reinforcement Learning for Distributed Context-Aware SFC Deployment and Migration in 6G Networks. CNSM 2025: 1-7 - [c6]Jinxuan Chen, Eric Samikwa, Torsten Braun, Kaushik R. Chowdhury:
FedAttention: Federated Attention-Based Fusion Learning for Multi-Modal Beamforming in IoV. ICC 2025: 6076-6081 - [c5]Jinxuan Chen, Eric Samikwa, Torsten Braun, Kaushik R. Chowdhury:
DRFSL: Deep Reinforced Federated Split Learning for Multi-Modal Beamforming in IoV. VTC2025-Spring 2025: 1-7 - 2024
- [j3]Haitham Afifi, Sabrina Pochaba, Andreas Boltres, Dominic Laniewski, Janek Haberer, Leonard Paeleke, Reza Poorzare, Daniel Stolpmann, Nikolas Wehner, Adrian Redder, Eric Samikwa, Michael Seufert:
Machine Learning With Computer Networks: Techniques, Datasets, and Models. IEEE Access 12: 54673-54720 (2024) - [j2]Eric Samikwa, Antonio Di Maio, Torsten Braun:
DISNET: Distributed Micro-Split Deep Learning in Heterogeneous Dynamic IoT. IEEE Internet Things J. 11(4): 6199-6216 (2024) - 2023
- [c4]Dimitris Xenakis, Eric Samikwa, Jesutofunmi Ajayi, Antonio Di Maio, Torsten Braun, Katja Schlegel:
Towards Personality Detection and Prediction using Smartphone Sensor Data. MedComNet 2023: 121-130 - [c3]Eric Samikwa, Jakob Schärer, Torsten Braun, Antonio Di Maio:
Machine Learning-based Energy Optimisation in Smart City Internet of Things. MobiHoc 2023: 364-369 - [d1]Eric Samikwa, Jakob Schärer, Torsten Braun, Antonio Di Maio:
Machine Learning-based Energy Optimisation in Smart City Internet of Things. Zenodo, 2023
- 2022
- [j1]Eric Samikwa, Antonio Di Maio, Torsten Braun:
ARES: Adaptive Resource-Aware Split Learning for Internet of Things. Comput. Networks 218: 109380 (2022) - [c2]Eric Samikwa, Antonio Di Maio, Torsten Braun:
Adaptive Early Exit of Computation for Energy-Efficient and Low-Latency Machine Learning over IoT Networks. CCNC 2022: 200-206 - 2020
- [c1]Eric Samikwa, Thiemo Voigt, Joakim Eriksson:
Flood Prediction Using IoT and Artificial Neural Networks with Edge Computing. iThings/GreenCom/CPSCom/SmartData/Cybermatics 2020: 234-240
Coauthor Index
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